Ecological stoichiometry influences phytoplankton alpha and beta diversity rather than the community stability in subtropical bay

Abstract Numerous studies have shown that changes in environmental factors can significantly impact and shift the structure of phytoplankton communities in marine ecosystems. However, little is known about the association between the ecological stoichiometry of seawater nutrients and phytoplankton community diversity and stability in subtropical bays. Therefore, we investigated the relationship between the phytoplankton community assemblage and seasonal variation in the Beibu Gulf, South China Sea. In this study, we found that the abundance of Bacillariophyceae in spring was relatively greater than in other seasons, whereas the abundance of Coscinodiscophyceae was relatively low in spring and winter but greatly increased in summer and autumn. Values of the alpha diversity indices gradually increased from spring to winter, revealing that seasonal variations shifted the phytoplankton community structure. The regression lines between the average variation degree and the Shannon index and Bray–Curtis dissimilarity values showed significantly positive correlations, indicating that high diversity was beneficial to maintaining community stability. In addition, the ecological stoichiometry of nutrients exhibited significantly positive associations with Shannon index and Bray–Curtis dissimilarity, demonstrating that ecological stoichiometry can significantly influence the alpha and beta diversity of phytoplankton communities. The C:N:P ratio was not statistically significantly correlated with average variation degree, suggesting that ecological stoichiometry rarely impacted the community stability. Temperature, nitrate, dissolved inorganic phosphorous, and total dissolved phosphorus were the main drivers of the phytoplankton community assemblage. The results of this study provide new perspectives about what influences phytoplankton community structure and the association between ecological stoichiometry, community diversity, and stability in response to environmental changes.


| INTRODUC TI ON
Phytoplankton are the most important primary producers in marine ecosystems and play a crucial role in biogeochemical cycling and food webs (Field et al., 1998;Li et al., 2021). Phytoplankton are the first link in the trophic chain and perform the significant function of organic material transfer between all abiotic and biotic components of the oceanographic environment (Staniszewska et al., 2015). The highly diverse nature of marine phytoplankton helps maintain the ocean's ecological balance because these species contribute to stability of material and energy cycling in marine ecosystems (Kosek et al., 2016).
Phytoplankton freely live in marine seawater, and thus their community structure and diversity are easily susceptible to environmental perturbations (Kosek et al., 2016;Li et al., 2021;Liang et al., 2020).
Temperature acts as an important environmental factor to affect the phytoplankton community structure. For example, it could regulate Antarctic phytoplankton community composition and size structure (Biggs et al., 2019). The general trend of phytoplankton succession and the community were interrupted due to the effects of elevated temperatures of thermal discharge from a power plant on coastal waters of the Bohai Sea off Qinhuangdao, China (Dong et al., 2021).
Nutrient factors also play a crucial role in affecting the phytoplankton community. Nutrient variables were found to significantly influence the phytoplankton community structure in every season in the estuary around Luoyuan Bay, China (Pan et al., 2017). Microcosm experiments conducted in Sanya Bay in the northern South China Sea revealed that enhancement of dissolved organic carbon (DOC) content could lead to a shift in the phytoplankton community and composition (Liao et al., 2019). Seasonal variability of nitrogen content influenced and regulated phytoplankton community structure in the eastern Arabian Sea (Shetye et al., 2019). Therefore, water temperature and nutrient content jointly influenced the marine phytoplankton community structure (Fu et al., 2016;Patoucheas et al., 2021;Zhang et al., 2016).
These shifts in phytoplankton composition and structure in response to environmental disturbance result in two main types of deterministic and stochastic processes in aquatic ecosystems (Bandyopadhyay et al., 2008;Jang & Allen, 2015;Mandal & Banerjee, 2013;Xue et al., 2018). The community stability has been attributed principally to species diversity because the general consensus is that biodiversity has positive effects on the community stability (Loreau & de Mazancourt, 2013;Vallina et al., 2017;Xun et al., 2021). The community stability can be evaluated using average variation degree (AVD), which is calculated as the degree of deviation from the average value of the relative abundance of normally distributed operational taxonomic units (OTUs) in variable environmental conditions, and a low AVD value represents high community stability (Xun et al., 2021). Similarly, phytoplankton community stability might be primarily determined by phytoplankton species diversity, which requires evaluation of the relationship between phytoplankton diversity and stability in marine ecosystems. Meanwhile, assessment of ecological stoichiometry (ES) mainly focuses on chemical elements (carbon (C), nitrogen (N), and phosphorous (P)) in components as well as interactions and processes in ecosystems, and it is beneficial for understanding the effect of human activities on the balance and biogeochemical cycling of bio-elements in oceanographic ecosystems (Babbin et al., 2014;Bradshaw et al., 2012;Chen et al., 2021). Consequently, the number of ES studies has increased rapidly in recent years (Sardans et al., 2021). For example, Hillebrand et al. (2013) found that N:P ratios of phytoplankton decreased as their growth rate increased and variance decreased, which means that fast-growing phytoplankton contained more P and had a simpler elemental composition (Hillebrand et al., 2013;Sardans et al., 2021).
However, little is known about the association between the ecological stoichiometry of seawater nutrients and phytoplankton community diversity and stability in subtropical bays.
To better understand the key factors that regulate shifts in phytoplankton structure and community stability at spatio-temporal scales, this study analyzed seawater samples from the subtropical coastal waters of Beibu Gulf during four seasons using highthroughput sequencing technology in order to (a) evaluate changes in the community structure of marine phytoplankton among seasons, (b) elucidate the potential relationships between phytoplankton community stability and various environmental factors, and (c) uncover the key factors that impact phytoplankton community stability in this subtropical bay. Our hypothesis was that the ecological stoichiometry of seawater nutrients might influence phytoplankton community diversity and stability in the subtropical bays.
Five seawater samples were collected at each of the five sampling sites. In total, 100 surface seawater samples were collected in the Maowei Sea during the study. Water temperature, pH, and salinity of each sample were measured using a portable meter (556 MPS; YSI, Yellow Springs, OH, USA). Following collection, samples were stored on ice for transport to the laboratory. For DNA extraction, 5 L of surface seawater were filtered sequentially through a 200 μm nuclepore polycarbonate filter to remove debris and larger organisms followed by a 0.22μm Millipore filter. The 0.22μm filters were stored at −20°C for subsequent analysis.

| Environmental factors and nutrient analysis
Concentrations of nitrate (NO − 3 ), nitrite (NO − 2 ), ammonium (NH + 4 ), and phosphate (PO 4 3− ) were measured using spectrophotometric and colorimetric methods (Han et al., 2012). The chlorophyll a (Chl-a) concentration was measured using spectrophotometry (American Public Health Association (APHA), 1999). Total organic carbon (TOC) content was measured using a TOC analyzer (TOC-VCPH). Chemical oxygen demand (COD) was detected using the alkaline KMnO 4 method. Dissolved oxygen (DO) was measured by the Winkler method using a digital DO meter (HQ30d, HACH, USA) (Shriwastav et al., 2017). Total dissolved nitrogen (TDN) and total dissolved phosphorus (TDP) contents were determined using a Lachat Quickchem 8500 flow injection analyzer (HACH). Dissolved inorganic nitrogen (DIN) content was calculated by summing the concentrations of NO − 2 , NO − 3 , and NH + 4 . Dissolved inorganic phosphorus (DIP) level was estimated using the concentration of PO 4 3− -P (Lai et al., 2014;Li et al., 2020). Seawater ES, including the ratios of C:N, C:P, N:P, and C:N:P, was calculated as molar ratios based on the TOC, TDN, and TDP. The results for the measured environmental parameters are shown in Table S1.

| DNA extraction, PCR amplification, and highthroughput sequencing
Total DNA was extracted from the filters using a DNeasy Power Raw sequences were processed and verified using the software package QIIME2 (Quality Insights Into Microbial Ecology) to remove sequences with primer mismatches or length < 275 bp, lowquality reads (quality scores <30), primers, and barcode sequences (Caporaso et al., 2010;Rai et al., 2019). Chimeric sequences were identified and eliminated using UCHIME (Edgar et al., 2011). The software was further subjected to OTU clustering based on 97% sequence similarity. The representative sequences were annotated using a local blastn program and the ribosomal database project database (Release 11) (Cole et al., 2014). Taxonomic assignments of phytoplankton were performed using an available rbcL sequence database generated from GenBank data. Sequencing data were obtained from all 100 samples, and a total of 1,687,350 sequences, with a mean of 16,874 ± 21,756 in each sample, were retained after removing low-quality reads (Table S2). The mean number of OTUs per sample was 298 ± 242 (Table S2). The coverage of sequencing samples was mostly >98% (Table S2). All phytoplankton sequencing data in FASTQ format were deposited in GenBank under access numbers ranging from SAMN20371288 to SAMN20371387 and Bioproject number PRJNA749375.

| Data and statistical analyses
To illustrate the scope of phytoplankton diversity, Good's coverage (C) was calculated as [1 -(n/N)], where n is the number of OTUs that was observed once and N is the total number of OTUs in the sample. The statistical analyses in this study were mainly performed in R with the vegan, picante, pheatmap, and psych' packages. Alpha and beta diversity, analysis of similarities (ANOSIM), and permutational multivariate analysis of variance (PERMANOVA) analyses were conducted using the vegan package, and the psych package was used for data correlation analysis. The difference analyses were conducted using one-way ANOVA. Correlation analyses were performed using Spearman's rank method. Alpha diversity was estimated using the Shannon, Simpson, Chao1, and observed number indices. Community comparison of phytoplankton assemblages (beta diversity) was conducted using Bray-Curtis distance and principal coordinate analysis (PCoA). Phytoplankton community stability was evaluated by AVD, which was calculated using the degree of deviation from the mean of the relative abundance of normally distributed OTUs among different seasons (Xun et al., 2021). Significant differences were defined as p < .05 or p < .01.

| Abundance and spatial distribution of phytoplankton communities in Beibu gulf during four seasons
The abundance of phytoplankton taxa in the communities was analyzed at the class level ( Figure S2). indices were highest in winter (Table S2). Moreover, all four alpha diversity indices had the highest median value in winter, which gradually decreased from winter to spring ( Figure 1).
The first two principal coordinates, PCo1 and PCo2, explained 30.11% and 23.61% of the total variance, respectively ( Figure 2).

| Relationship between AVD and Shannon index and Bray-Curtis dissimilarity in Beibu gulf during seasonal shifts
The relationships between AVD and the Shannon index and Bray-Curtis dissimilarity are shown as Figure 3. AVD was strongly F I G U R E 1 Alpha diversity indices (Shannon, Simpson, Chao1, and observed number) for samples collected in spring, summer, autumn, and winter. The values represent the mean of five samples for each group.

| Ecological stoichiometry effects on the phytoplankton community
The mean values of ES ratios (C:N, C:P, N:P, and C:N:P) were highest in summer, and they gradually decreased from summer to spring to their lowest values (Table S1). The Shannon index values were significantly positively correlated with all four ratios as follows: C:N (p < .05), C:P (p < .05), N:P and C:N:P (p < .01) (Figure 4). All four ratios also were significantly positively correlated with Bray-Curtis dissimilarity values (p < .001) ( Figure 5). In contrast, AVD was negatively correlated with all four ratios, but the correlation was only significantly negative for C:N (p < .05) ( Figure S3).

| Environmental factors that explain spatial variability in phytoplankton communities
The Mantel test and partial Mantel test revealed that environmental and biogeochemical factors and ratios of ES were significantly correlated with beta diversity of phytoplankton communities (Table 1).
Salinity and TDP were significantly positively correlated with phytoplankton community beta diversity at the whole combined sample level and at the individual seasonal sampling level (Table 1).
Generally, TDP, NO − 3 , DIP, and temperature were the main factors that drove the phytoplankton community diversity (Table 1).
Seawater properties (temperature, pH, salinity, Chl-a, dissolved oxygen, and COD), nutrients (NO − 2 , NO − 3 , NH + 4 , DIN, TDN, DIP, TDP, and TOC), and ratios of C:N, C:P, N:P, and C:N:P were able to explain approximately 75% of phytoplankton community variations ( Figure 6). In addition, seawater properties, nutrient variables, and ratio values could independently account for 15%, 25%, and 13% of the total variation, respectively. Additionally, mutual interactions F I G U R E 2 PCoA results showing the phytoplankton community variations based on Bray-Curtis distance matrices. Samples from spring, summer, autumn, and winter are labeled with red dots, green squares, blue rhombi, and purple triangles, respectively between seawater properties and nutrient variables were responsible for 12% of the variation, which was much higher than those between nutrients and ES ratios (2%) and seawater properties and ES ratios (3%).

| DISCUSS ION
In this study, we investigated variations in phytoplankton community structure in the subtropical Beibu Gulf area. We assessed phytoplankton community stability in response to environmental changes and identified the main drivers of the observed changes community structure. Bacillariophyceae, Coscinodiscophyceae, Mediophyceae, Fragilariophyceae, and Bangiophyceae were the dominant phytoplankton classes. Diatoms represent the richest group of autotrophic phytoplankton present in fresh, brackish, and marine waters worldwide, and they may be responsible for 20% of global photosynthetic carbon fixation in marine ecosystems (Mann et al., 2017). In our study, the greatest mean abundance of Bacillariophyceae occurred in spring and winter, which might be due to the relatively lower water temperature in these seasons in the Beibu Gulf area. Gogoi et al. (2021) also reported a negative correlation between Bacillariophyceae and water temperature in Sundarban waters. Interestingly, we found that mean abundance of Coscinodiscophyceae was higher in summer and autumn when the seawater temperature was elevated com-  (Xu et al., 2015). Gao et al. (2013) (Xu et al., 2015). In our study, the diversity of the phytoplankton community sharply decreased from winter to spring as the temperature decreased, which might be because some phytoplankton could not grow or survive at relatively low temperatures in the Beibu Gulf area (Yu et al., 2019). These Understanding the relationship between community diversity and stability under environmental perturbations in ecosystems is a crucial issue and is the subject of a long-standing debate (Guelzow et al., 2017;Loreau & de Mazancourt, 2013). Although there may be a single-species group that shows unstable dynamic changes in a diverse community, the consensus is that that diversity enhances community stability in ecosystems (McCann, 2000;Ptacnik et al., 2008). For marine phytoplankton, species composition and richness were important for increases of phytoplankton community stability (Corcoran & Boeing, 2012;Guelzow et al., 2017). Thus, our results indicated that higher alpha and beta diversity was benefi- problem (Li et al., 2020). Therefore, studies that consider different environmental, nutrient, and spatio-temporal scales in different areas should be conducted. In our study, we also found that ratios of C:N, C:P, N:P, and C:N:P were highest in summer, which may have been because the high growth rate of phytoplankton generated high biomass production under relatively high temperatures and because the massive amount of nutrients in the seawater were absorbed and utilized by the phytoplankton community (Zhou et al., 2021). We also found that the C:N ratio was significantly negatively correlated with AVD, whereas the negative correlations of C:P, N:P, and C:N:P ratios with AVD were not statistically significant. This result suggests that ES rarely impacted the stability of the phytoplankton community in the Beibu Gulf area.
Phytoplankton communities are susceptible to environmental disturbance. Temperature is an important environment factor that influences phytoplankton growth, metabolism, production, and community structure (Dong et al., 2021). Similar to our results, Lv et al. (2014) and Gogoi et al. (2021) both found that temperature was one of the crucial deterministic parameters impacting the phytoplankton community structure. The Netravathi-Gurupura estuary surrounded by several river inlets is similar with our study area and therefore is easily influenced by discharges and effluent nutrients from the rivers (Kumar et al., 2020). In agreement with our results, a significant positive correlation between phytoplankton composition and NO − 3 content was also observed, indicating that NO − 3 was an important driver of phytoplankton community structure (Kumar et al., 2020). DIP is frequently the limiting nutrient for phytoplankton growth in marine ecosystems and therefore is a key driver of phytoplankton community variation (Yuan et al., 2018;Zhang et al., 2020). Overall, our results demonstrate that these environmental and nutrient factors are essential to phytoplankton growth, metabolism, and biomass production and therefore could significantly shape the pattern of phytoplankton communities in the subtropical Beibu Gulf.

| CON CLUS IONS
Our results demonstrate that phytoplankton community structure undergoes seasonal variations in the Beibu Gulf. The low water temperatures in winter and spring seem to favor the growth of

CO N FLI C T S O F I NTE R E S T S
The authors declare that they have no conflicts of interests.

DATA AVA I L A B I L I T Y S TAT E M E N T
The datasets presented in this study can be found in NCBI SRA as BioProject PRJNA749375 under access numbers ranging from SAMN20371288 to SAMN20371387.